Multilayer perceptron network with modified sigmoid activation functions

Authors:
Tobias Ebert;Oliver Bänfer;Oliver Nelles
Affiliations:
University of Siegen, Department of Mechanical Engineering, Siegen, Germany;University of Siegen, Department of Mechanical Engineering, Siegen, Germany;University of Siegen, Department of Mechanical Engineering, Siegen, Germany
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

Citing 2
Cited 2

Multilayer feedforward networks are universal approximators

Neural Networks
Local Model Networks with Modified Parabolic Membership Functions

AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 01

A method to improve the performance of multilayer perceptron by utilizing various activation functions in the last hidden layer and the least squares method

Neural Processing Letters
Selection of activation functions in the last hidden layer of the multilayer perceptron

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Models in today's microcontrollers, e.g. engine control units, are realized with a multitude of characteristic curves and look-up tables. The increasing complexity of these models causes an exponential growth of the required calibration memory. Hence, neural networks, e.g. multilayer perceptron networks (MLP), which provide a solution for this problem, become more important for modeling. Usually sigmoid functions are used as membership functions. The calculation of the therefore necessary exponential function is very demanding on low performance microcontrollers. Thus in this paper a modified activation function for the efficient implementation of MLP networks is proposed. Their advantages compared to standard look-up tables are illustrated by the application of an intake manifold model of a combustion engine.